diff --git a/py/torch_tensorrt/dynamo/backend/backends.py b/py/torch_tensorrt/dynamo/backend/backends.py
index 8f6408492a..cf869562b6 100644
--- a/py/torch_tensorrt/dynamo/backend/backends.py
+++ b/py/torch_tensorrt/dynamo/backend/backends.py
@@ -12,6 +12,7 @@
     partition,
     get_submod_inputs,
 )
+from torch_tensorrt.dynamo.backend.utils import parse_dynamo_kwargs
 from torch_tensorrt.dynamo.backend.conversion import convert_module
 
 from torch._dynamo.backends.common import fake_tensor_unsupported
@@ -25,22 +26,20 @@
 @td.register_backend(name="torch_tensorrt")
 @fake_tensor_unsupported
 def torch_tensorrt_backend(
-    gm: torch.fx.GraphModule,
-    sample_inputs: Sequence[torch.Tensor],
-    settings: CompilationSettings = CompilationSettings(),
+    gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor], **kwargs
 ):
     DEFAULT_BACKEND = aot_torch_tensorrt_aten_backend
 
-    return DEFAULT_BACKEND(gm, sample_inputs, settings=settings)
+    return DEFAULT_BACKEND(gm, sample_inputs, **kwargs)
 
 
 @td.register_backend(name="aot_torch_tensorrt_aten")
 @fake_tensor_unsupported
 def aot_torch_tensorrt_aten_backend(
-    gm: torch.fx.GraphModule,
-    sample_inputs: Sequence[torch.Tensor],
-    settings: CompilationSettings = CompilationSettings(),
+    gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor], **kwargs
 ):
+    settings = parse_dynamo_kwargs(kwargs)
+
     custom_backend = partial(
         _pretraced_backend,
         settings=settings,
diff --git a/py/torch_tensorrt/dynamo/backend/utils.py b/py/torch_tensorrt/dynamo/backend/utils.py
index e6e22d5f96..9396373790 100644
--- a/py/torch_tensorrt/dynamo/backend/utils.py
+++ b/py/torch_tensorrt/dynamo/backend/utils.py
@@ -1,9 +1,15 @@
 import torch
+import logging
+from dataclasses import replace, fields
 
+from torch_tensorrt.dynamo.backend._settings import CompilationSettings
 from typing import Any, Union, Sequence, Dict
 from torch_tensorrt import _Input, Device
 
 
+logger = logging.getLogger(__name__)
+
+
 def prepare_inputs(
     inputs: Union[_Input.Input, torch.Tensor, Sequence, Dict],
     device: torch.device = torch.device("cuda"),
@@ -66,3 +72,36 @@ def prepare_device(device: Union[Device, torch.device]) -> torch.device:
         )
 
     return device
+
+
+def parse_dynamo_kwargs(kwargs: Dict) -> CompilationSettings:
+    """Parses the kwargs field of a Dynamo backend
+
+    Args:
+        kwargs: Keyword arguments dictionary provided to the backend
+    Returns:
+        CompilationSettings object with relevant kwargs
+    """
+
+    # Initialize an empty CompilationSettings object
+    settings = CompilationSettings()
+
+    # If the user specifies keyword args, overwrite those fields in settings
+    # Validate all specified kwargs to ensure they are true fields of the dataclass
+    #
+    # Note: kwargs provided by torch.compile are wrapped in the "options" key
+    if kwargs:
+        if "options" in kwargs and len(kwargs) == 1:
+            kwargs = kwargs["options"]
+
+        valid_attrs = {attr.name for attr in fields(settings)}
+        valid_kwargs = {k: v for k, v in kwargs.items() if k in valid_attrs}
+        settings = replace(settings, **valid_kwargs)
+
+    # Enable debug/verbose mode if requested
+    if settings.debug:
+        logger.setLevel(logging.DEBUG)
+
+    logger.debug(f"Compiling with Settings:\n{settings}")
+
+    return settings